1. Field of the Invention
The present invention relates to a fuzzy processor for pattern recognition, more particularly, to a programmable and expandable fuzzy processor for comparing a to-be-recognized pattern with a plurality of standard patterns.
2. Description of Related Art
The idea of fuzzy logic was introduced by L. A. Zadeh in 1965, which plays an important role in the field of computer science and has been successfully applied in many applications. Fuzzy logic is commonly implemented in computer software. However, because software fuzzy logic can not satisfy many applications requiring real-time processing, a design for hardware fuzzy logic has become an important research initiative.
Because of the imprecise, vague and incomplete nature of available information about a collection of objects, people usually has to proceed in fuzzy inference and judgement, which forms the physical basis for the fuzzy mathematics applied in pattern recognition. The principle rule of fuzzy pattern recognition is the maximum membership degree rule. In actual application, a standard pattern usually has a plurality of fuzzy features. If there are n standard patterns each having m fuzzy features and the jth fuzzy feature of the ith pattern is Aij, where i=1,2, . . . ,n; j=1,2, . . . ,m, then each standard pattern Ai is a fuzzy vector Ai=&lt;Ai1, Ai2, . . . . , Aim&gt;, 1&lt;i&lt;n. Assuming that u=(u.sub.1, u.sub.2, . . . ,U.sub.m) is a pattern to be recognized, each member of u, that is each u.sub.j, corresponds to a fuzzy feature. If there exists an i .epsilon. {1,2, . . . ,n} such that .mu..sub.Ai (u) =max{.mu..sub.A1 (u), .mu..sub.A2 (U), . . . , .mu..sub.An (u)} then u relatively belongs to Ai, wherein it is assumed that .mu..sub.Ai (u)=M.sub.m (.mu..sub.Ai1 (u.sub.1), .mu..sub.Ai2 (u.sub.2), . . . , .mu..sub.Aim(u.sub.m)), and M.sub.m ( ) is a synthesis function.
The above expression of .mu..sub.Ai (u)=max{.mu..sub.A1 (u), .mu..sub.A2 (u), . . . , .mu..sub.An (u)} discloses the recognition rule for conventional fuzzy pattern having multiple features. Currently, most of the hardware implementations for fuzzy pattern recognition are based on the rule which only finds the closest standard pattern for the to-be-recognized pattern. However, with the rise in system complexity, the increase in the number of standard patterns and especially the development of expanded systems with multiple stages, the above hardware implementation of fuzzy logic appears to be unsatisfactory. To enhance the system performance, it is necessary to find two or more of the closest standard patterns for the to-be-recognized pattern according to the synthesis membership degrees between the to-be-recognized pattern and the standard patterns. Therefore, a novel fuzzy processor is set forth hereinafter, which can sequentially output the synthesis membership degrees as well as the corresponding standard patterns in an order of magnitude. Accordingly, the h closest standard patterns can be found sequentially where 1.ltoreq.h.ltoreq.n. This will greatly improve the system performance by increasing the recognition rate and reprocessing and reusing data in a multi-stage expanded system.
There are many choices for the synthesis functions. The most frequently used are the minimum-finding function, {character pullout}.sub.j (X)={character pullout}.sub.j x.sub.j for j=1 to m, and the summation function, .SIGMA.X=.SIGMA.x.sub.j for j=1 to m. The minimum-finding function is not suitable for pattern recognition since it only emphasizes a local feature and neglects the other features. The summation function is able to include the effects of all features whereby it is suitable for pattern recognition. Therefore, the summation function is adopted by the fuzzy processor in accordance with the present invention.